A Systematic Review of Machine Learning, Deep Learning, and Transfer Learning Methods for Skin Disease Classification

Abstract

Skin diseases pose significant global health challenges, with artificial intelligence emerging as a revolutionary tool in dermatological diagnostics. This paper provides a comprehensive analysis of machine learning approaches in skin disease detection, focusing on traditional machine learning, deep learning, and transfer learning methodologies. Traditional machine learning methods like Support Vector Machines (SVM) and Random Forests effectively process structured clinical data and extracted features. In contrast, deep learning models, particularly Convolutional Neural Networks (CNNs), excel at processing raw dermatological images. Transfer learning has proven especially powerful, utilizing pre-trained models like ResNet, VGG, and Inception, which are initially trained on large datasets like ImageNet and then fine-tuned for dermatological applications. This approach significantly reduces required training data and development time while improving performance, with accuracy rates ranging from 50-100% .Current research focuses on developing real-time AI models, multimodal analysis systems, and diagnostic tools. While these technologies show promise for clinical decision-making support, challenges remain in data standardization, reducing algorithmic biases, and ensuring consistent performance across diverse patient populations. The paper concludes by addressing critical challenges and future directions in automated skin disease detection technology.